 Hello, I welcome you all once again to my channel for your education and Dr. Rashmi Singh, Assistant Professor, Department of Education, Assistant Nagar Study College, University of Palahak. And in the series of discussing issues over statistics, this time I am going to discuss about measures of central tendency. This is a very important and common topic on the basis of central tendency. In this video, we will only discuss its concept, theory, and how to remove its computation in the next videos. Okay, so let's start. Hmm, measures of central tendency, that is, what is the basis of central tendency? For example, in the name of statistics, the data we have received from statistics, what is the basis of the data? They want to come to the center. Their affection for the center is affinity. That is the basic concept of measures of central tendency. What are the eyes whose priority is to come to the center? This is the rule of law. If you want to understand it in one line. So, what it says is that measures of central tendency provides a single value. That is, the values of central tendency give us a single value. A single value that indicates the general magnitude of the data and this single value provides information about the characteristics of the data by identifying the value at or near the central location of the data. See, in this definition, you can fully understand what is the map of central tendency. What it says is that the map of central tendency gives us a single value that indicates the general magnitude of the data. That is, the general magnitude of the data is the value at or near the central location of the data. And what does this value give us? It gives us information about the characteristics of the data. The characteristics of the data. That is, the eyes that we get give us information about the characteristics of the data by identifying the value at or near the central location of the data. How does it give us information about the eyes? It gives us information about the mind that is coming in the middle of it. That is why it says the map of central tendency. That is the map whose priority is to come to the center. So, you will be able to understand the map of central tendency. So, you will be able to understand the map of central tendency. So, we will be able to tell you what is the magnitude of the data at the center and what is the general information about it. That is the value of the data. So, instead of being afraid of the statistics, we will first understand it. So, what is the next step? We will have to come out of it. This is the second term. It is the second step. We have to come up with half-questions or numerical questions. The first step is to understand the concept. Without understanding the concept, you will not be able to ask questions. So, this definition was made in 2011 by Borden and Borden. It is a very strict definition. Then, what is the minimum of the king in 2013? It says described measures of central tendency as a summary field. It says that the maps of central nature give us a mark in the form of a syringe that helps in describing a central location for a certain group of scores. It helps us to get a certain group of scores. We have been given a sample of all the scores. This summary figure tells us about the center of this situation. What summary figure? The measures of central tendency. Which are? You will know. I will tell you now. Then, Tate described in 1995 that measures of central tendency as a sort of, it should be a sort of average or typical value of the items in the series and its function is to summarize the series in terms of this average value. It says that it is telling us the average. It is telling us the average of the items. And it is telling us the typical value of the items in the series. And what is its work? To summarize the series in terms of this average value. That is to say, whatever series is given to us, whatever points we get, whatever scores we get, it summarizes it in terms of its average value. Who? The measures of central tendency. So, what is the work? What are its functions? They provide a summary figure with the help of which the central location of the whole data can be explained. That is to say, the most important thing is that we are given a single value. We will be given a figure in the form of a summary in the form of Saransh, with the help of which we will be able to tell that we can explain the central location of the entire data. The first and the most important thing. Then, large amounts of data can be easily reduced to a single figure. We got a very large amount of data. We told it in one figure that almost the magnitude of the data, its information, its location, its distribution, what is it? Then, when the mean is computed for a certain sample, we will be able to tell the population of the entire data. When we talk about the mean, let me tell you in the last part, what are the three main areas of the central region. So, if we talk about a sample or a population, what is the population? The whole individual, the basic, whatever we have taken, the organization, whatever we have taken for the study, everything comes into the population, which is called understanding. For example, when we take out a representative group, we take out a representative group, we take out a sample or a sample or a sample. So, it is said that when you take out a mean for a certain sample, you can also tell that you can tell about the mean of the population. Then, the results obtained from computing measures of central tendency will help in making certain decisions for policy making, marketing, sales and so on. It is said that if you take out measures of central tendency, then you can make direct decisions for a certain score. So, you will also be helped to take out decision making, such as, to make policy, marketing, sales and so on. Then, comparison can be carried out based on single figures computed with the help of measures of central tendency. When we take out measures of central tendency, we can also divide a number of the eye groups. Then, the three measures of central tendency are 3 most popular, 3 most popular measures of central tendency and a lot of people will ask you if you know anything about the statistics then the mean median mode will be there or the mean median mode will be there, so the mean median mode is the basic for you. Ok so these 3 we will get on the map of the central region which will tell us about the central region of the eyes. Let's take a look at the mean median mode. The mean median mode is one of the most commonly used measures of central tendency. The most commonly used measure of central tendency is the mean median mode. It is often referred to as average and it is also generally referred to as standard. It can also be termed as one of the most sensitive measures of central tendency is all this force in a data taken into consideration when it is computed. And you can say that it is also the most sensitive measure of central tendency. Why? Because when you calculate it, you consider all the scores in it. All the scores are taken, no one leaves them. Mean is the total of all the scores in data divided by the total number of scores. And how is it taken out? When everyone in their childhood taught in Garnit's lecture that how is it taken out? The more scores they give, the more scores they give and the more scores they give, they run away from their numbers. Like 2, 4, 6, 10, 8, 2, 4, 6, 8, 10, 12, 14, 16. All these are taken out and in 2, 4, 6, 8, 10, 12, 14, 16, whatever 7, 8, 8, 8, 8 are given, they run away from them. So you will get to know what is average. Then what is median? The median is a point. The thing to pay attention to is that the index of the central tendency is a special thing. The average was that the index of the central tendency is more than the index of the central tendency. Whereas the median is telling us about the index. Madhika says that it is a point in any distribution. That is, it is a point in any distribution. Below and above which lie half of the scores, which are above and below half of the scores are left. That is, there are a lot of eyes. If you are given the ascending or descending order, then that is the index of the central tendency. Which is above and below. That is, it is the middle point. You can say that the middle point is called the median. That is why it is denoted from P50. The median is also referred to as P50. What is P? What is the point of 50? In the middle. It will be between 100 and 50. That is why. As stated by Bordens and Abbott, the median is the middle score in an ordered distribution. That is, you have to pay attention to the order of the distribution. That is, you will not do it in a better, half hazard distribution. You can give any number to it. You can make it in order, either in order or in order. And then you can tell what we generally increase. The ascending order. You can tell that the middle point is above and below half of the scores. If scores are to be arranged in either ascending or descending order, then the middle score in this distribution is then identified as a median. Whatever we have given to the ascending order, we can arrange it in ascending order. The point of the middle point will be our median. It will be the middle point. Then the mode. It is the easiest and the least used. Mode is denoted by the symbol MO. What is MO? It is the measure of your central tendency. And what is O? It is for the mode. What is mode? Bahu Lak. Mode is the score in a distribution that occurs most frequently. It is the easiest. You can just look at it and tell which score is used the most. It is the highest. For example, if you have two eyes, six, four, eight, two, five, six, two. So it is coming again and again. What is two? It is Bahu Lak. It is coming again and again. It is the highest. Certain distributions can be bimodal, trimodal and multimodal as well. Some angles can be bimodal. There are two angles that are coming again and again. Trimodal means three angles that are coming again and again. Multi-modal means many angles that are coming again and again. This type of data can also be. Though if the scores in a distribution greatly vary, then there may be no mode. This can also happen. That you get such an angle that no angle is repeating. It does not matter. Everything is completely different from each other. So when we get an angle, it does not mean that it is not Bahu Lak. That is why Bahu Lak is easy to calculate. It is the easiest to compute. But it does not mean that it is sensitive. You do not get much information about the angle. And many times it can happen that you did not get it. Okay. Then there is your mean. Now look at the meaning properly. What is the mean? The mean is sensitive to the actual position of each and every score in a distribution. And if another score is included in the distribution, then the mean or average of that distribution will change. The mean is the most sensitive. The actual position tells us the distribution of each and every score. And the moment you add any score, the mean will change. Because we all have to join. And if everyone has an angle, then the mean will change. So the mean is the most sensitive. And the mean denotes a balanced point of any distribution. And the total of positive deviations from the mean is equal to the negative deviations from the mean. Look at this. What is the mean? That is the mean. So all the angles are not the same. All angles are the same. That means if we all join, then the mean will change. That means actually, every angle is showing deviations. So they are saying that the positive deviation of the mean is the same as the negative deviation of the mean. That means if you all join the deviations, that means the positive deviation of the mean is the minus of the negative deviation. So the point of the mean is that the rest of the angles will be less than that or more than that. So the positive deviation and the negative deviation are equal to the mean. And the mean is especially effective when we want the measure of centrality to reflect the sum of this course. And this is the most effective when we have to find out the value of the central point of the mean if we want to find out the value of the mean. And what is the benefit of this? Easy to calculate. You can easily calculate this. All these scores in the distribution are considered when the mean is computed. When the mean is taken out, then we have to take all the angles and further mathematical calculations can be carried out on the basis of the mean. If you want to take out the standard deviation, the mean, then you can take out something else as well. You can also calculate the value of the mean. And what are the limitations? Outliers and extreme values can have an impact on me. Assume that you have given 2, 4, 6, 8, 10, 12, 14, 16, 50. Now 50 is very much. Outlier is an extreme score. So if you take it out, then you will get around 50. But how are the rest of the angles? 2, 4, 6, 8, 10, 12, 14, these are very far from 50. So as soon as there is an outlier, then the mean is deviate and it does not give us the right reflection of the data. So this is its limitation. It is not suitable for data that is skewed or is very asymmetrical. It is not suitable for that kind of angle. 3, 2, 6, 5, 10, 9, 3 data is given and they have to take out the mean. So the mean will be somewhere around 500 gas. Whereas the data was 3. So if you get the data, then the mean is not suitable for that. Because it will not do the right representation. The median is less sensitive to extreme scores and outliers. It is said that compared to the mean, compared to the average, the average is less sensitive. For extreme scores, why? Because it does not have to be joined. We just have to tell that what is the middle point for extreme scores? If there are 100, 1, 2, 3, 4, 5, 6, 8, 10, 100, then it does not matter to us. The middle point will have to be there. Then when a distribution is skewed or asymmetrical, the median can be adequately used. When your distribution is skewed, meaning that it is not possible to use the mean, but it is possible to use the mean because the mean does not have to take any of these scores. That is the actual score at one end of the distribution is not known then the median can be computed. What does open-ended mean? As we talked about it earlier, the statistical series of series, if it is 0 to 10, then you are aware that the first rank is 0. But if it says less than 10, that is open-ended, more than 50, then you are open-ended. You do not know the actual score, then you can remove the median. You do not know the actual score. You cannot calculate the median but you can do the median. Okay? What is the benefit of this? That it will not affect the outlier or extreme score. It will not affect the outlier or extreme score. In certain cases, it is possible to identify the median through inspection as well as graphically. We can only look at the graph and tell the median what are the limitations not based on every score in the distribution. This is not the basis for the outlier or extreme score, and it can be affected by sampling fluctuations and thus can be termed as less stable than the mean. And since sampling, that is how you have made the sample, it affects it and it depends on it. So this is less than the median. So this is about the median. Then the mode. The easiest mode is compute than mean and median. In mean and median, you still have to see the mode. You can only see the difference in the number of points. And mode is not affected by outliers or extreme scores. This is not affected by outlier or extreme score like the median. So what is the benefit of this? It can also be determined by mere inspection. It is not affected by outlier or extreme scores. But it is a limitation. It is sometimes possible that the scores in the data vary from each other and in such cases the data may have no more. This can happen that you may not get any such result. No one has more than one frequency. They are completely different from each other. So there is no benefit in that. It is not based on the whole distribution. This is not based on the whole distribution. And sampling fluctuations have an impact on mode. The fluctuation of the sampling fluctuation means the sampling is changing. It is varying. That is the difference. That is why it is lacking. So how can we take out all this? The measures of central tendency. The measures of central tendency are those measures whose tendency is to come towards the center. The central tendency tells us whether the number of eyes is less or the number of eyes is more. The media tells us that these are the eyes where the eyes are equal. They do not consider the eyes to be equal. For example, if we give 11 eyes, then the center point will tell us that there are 5 eyes above and 5 below. Similarly, when we learn computation, we will learn how to know the center point when there is an even number. The 5th and 6th the 4th or 5th which is in the middle, will tell us what is the center point. So this way, it depends on the eyes. It does not depend on the mind. And what is the fluctuation? The fluctuation is the easiest. The fluctuation can be more than one eye So this is the fluctuation. After fluctuation, the question is whether the data or the grouped data or the eye of the face or the eye of the eye is the main media and the mode. So the question is what is the formula or the individual series or the grouped data or the short method or the long method or the formula you have to keep in mind if you have to do something then the main media mode will be written. So first, you should understand the concept. What is the measure of central redundancy and what is the concept of the main media mode and how they differ from each other and where there is similarity. So in the next videos I will cover how the main media mode is taken out. Okay? So thank you and don't forget to like and subscribe my channel ExploreITUATION. I have done from my side.